---
title: "NYC Restaurant Inspections Dashboard"
author: "Xuange Liang (xl3493)"
output:
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    source_code: embed
---

<!-- ```{r setup, include=FALSE}
install.packages("tidyverse")
install.packages("plotly")
install.packages("devtools")
devtools::install_github("p8105/p8105.datasets")
``` -->

```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
library(plotly)
library(p8105.datasets)

# Load and clean data
data("rest_inspec")

# Filter and clean the dataset
# Focus on recent data and common cuisines
rest_clean <- rest_inspec %>%
  filter(
    !is.na(score),
    !is.na(grade),
    grade %in% c("A", "B", "C"),
    boro != "Missing",
    boro != "0",  # Remove invalid borough code
    !is.na(cuisine_description)
  ) %>%
  mutate(
    inspection_date = as.Date(inspection_date),
    year = year(inspection_date)
  ) %>%
  filter(year >= 2015, year <= 2017)

# Get top 10 cuisines by number of restaurants
top_cuisines <- rest_clean %>%
  count(cuisine_description, sort = TRUE) %>%
  head(10) %>%
  pull(cuisine_description)

# Filter for top cuisines
rest_filtered <- rest_clean %>%
  filter(cuisine_description %in% top_cuisines) %>%
  mutate(
    cuisine_display = if_else(
      str_detect(cuisine_description, regex("^Latin", ignore_case = TRUE)),
      "Latin",
      cuisine_description
    )
  )
```

Column {data-width=500}
-----------------------------------------------------------------------

### Distribution of Restaurant Grades by Borough

```{r}
# Create grouped bar chart
grade_by_boro <- rest_filtered %>%
  count(boro, grade) %>%
  group_by(boro) %>%
  mutate(percentage = n / sum(n) * 100)

plot_bar <- plot_ly(
  data = grade_by_boro,
  x = ~boro,
  y = ~percentage,
  color = ~grade,
  type = "bar",
  colors = c("A" = "#2E7D32", "B" = "#FFA726", "C" = "#D32F2F"),
  text = ~paste0(round(percentage, 1), "%"),
  textposition = "auto",
  hovertemplate = paste(
    "<b>Borough:</b> %{x}<br>",
    "<b>Grade:</b> %{fullData.name}<br>",
    "<b>Percentage:</b> %{y:.1f}%<br>",
    "<extra></extra>"
  )
) %>%
  layout(
    barmode = "stack",
    xaxis = list(title = "Borough"),
    yaxis = list(title = "Percentage (%)"),
    legend = list(title = list(text = "Grade")),
    hovermode = "closest"
  )

plot_bar
```

### Inspection Scores by Top 10 Cuisine Types

```{r}
# Create box plot for cuisine types
plot_box <- plot_ly(
  data = rest_filtered,
  x = ~reorder(cuisine_display, score, median),
  y = ~score,
  color = ~cuisine_display,
  type = "box",
  colors = "Set3",
  hovertemplate = paste(
    "<b>Cuisine:</b> %{x}<br>",
    "<b>Score:</b> %{y}<br>",
    "<extra></extra>"
  )
) %>%
  layout(
    xaxis = list(title = "Cuisine Type", tickangle = -45),
    yaxis = list(title = "Inspection Score"),
    showlegend = FALSE,
    hovermode = "closest"
  )

plot_box
```

Column {data-width=500}
-----------------------------------------------------------------------

### Average Inspection Score Trends Over Time by Borough

```{r}
# Create line plot showing trends over time
score_trends <- rest_filtered %>%
  group_by(boro, year, month = month(inspection_date)) %>%
  summarize(avg_score = mean(score, na.rm = TRUE), .groups = "drop") %>%
  mutate(date = as.Date(paste(year, month, "01", sep = "-")))

plot_line <- plot_ly(
  data = score_trends,
  x = ~date,
  y = ~avg_score,
  color = ~boro,
  type = "scatter",
  mode = "lines+markers",
  colors = "Set2",
  hovertemplate = paste(
    "<b>Borough:</b> %{fullData.name}<br>",
    "<b>Date:</b> %{x|%Y-%m}<br>",
    "<b>Avg Score:</b> %{y:.1f}<br>",
    "<extra></extra>"
  )
) %>%
  layout(
    xaxis = list(title = "Date"),
    yaxis = list(title = "Average Inspection Score"),
    legend = list(title = list(text = "Borough")),
    hovermode = "closest"
  )

plot_line
```

### Score Distribution: Top 5 Cuisines vs Borough

```{r}
# Create scatter plot with top 5 cuisines
top_5_cuisines <- rest_clean %>%
  count(cuisine_description, sort = TRUE) %>%
  head(5) %>%
  pull(cuisine_description)

rest_scatter <- rest_clean %>%
  filter(cuisine_description %in% top_5_cuisines) %>%
  group_by(cuisine_description, boro) %>%
  summarize(
    avg_score = mean(score, na.rm = TRUE),
    count = n(),
    .groups = "drop"
  )

plot_scatter <- plot_ly(
  data = rest_scatter,
  x = ~avg_score,
  y = ~boro,
  size = ~count,
  color = ~cuisine_description,
  type = "scatter",
  mode = "markers",
  colors = "Paired",
  marker = list(
    sizemode = "diameter",
    opacity = 0.7,
    line = list(width = 1, color = "white")
  ),
  hovertemplate = paste(
    "<b>Cuisine:</b> %{fullData.name}<br>",
    "<b>Borough:</b> %{y}<br>",
    "<b>Avg Score:</b> %{x:.1f}<br>",
    "<b>Count:</b> %{marker.size}<br>",
    "<extra></extra>"
  )
) %>%
  layout(
    xaxis = list(title = "Average Inspection Score"),
    yaxis = list(title = "Borough"),
    legend = list(title = list(text = "Cuisine Type")),
    hovermode = "closest"
  )

plot_scatter
```